A laser point cloud indicating distance and orientation measured by a laser scanner reproduces the 3D shape of a feature on the ground. A larger number of laser points make a 3D shape more accurate, and therefore a vast number of laser points are acquired.
However, the laser point cloud includes a point cloud obtained by measuring a feature that is not intended for reproduction. Therefore, there is a need of extracting the laser point cloud obtained by measuring a feature intended for reproduction from massive laser points.
Laser point clouds have been extracted by the following methods:
(1) A laser point cloud is viewed in a three dimensional manner, and a point is extracted if necessary with visual confirmation; and
(2) A laser point cloud is superimposed on a camera image on a display to help identify a target feature, and a point is extracted if necessary with visual confirmation.
The method (1) however poses the following problems, for example:
(A) Laser points need to be designated one by one for extraction; and
(B) Extracted laser points cannot be used directly on CAD (Computer Aide Design).
The method (2) poses the following problems, for example:
(A) A target feature can be identified only by a laser point cloud showing points arranged in the direction of the field of vision of the camera;
(B) It takes time and labor to select an appropriate camera image; and
(C) It is hard to identify the place where a target feature is located.
Those introduced methods require visual confirmation for each point to be extracted, which takes time. On the other hand, automatic recognition techniques have been under development. With the automatic recognition, recognizable features are limited and the recognition rate is not sufficient enough. Also, visual confirmation is required for correction.